Local tone mapping for symbol reading

ABSTRACT

Embodiments related to local tone mapping for symbol reading. A local pixel neighborhood metric is determined for at least one raw pixel in a region-of-interest, which identifies on one or more raw pixels near the at least one raw pixel. A local mapping function is determined for the at least one raw pixel that maps the value of the raw pixel to a mapped pixel value with a mapped bit depth that is smaller than the bit depth associated with the raw image. The local mapping function is based on a value of at least one other raw pixel near the at least one raw pixel within the local pixel neighborhood metric, and at least one parameter determined based on the raw image. A mapped image is computed for the region-of-interest by applying the local mapping function to the raw image.

RELATED APPLICATIONS

This Application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application Ser. No. 62/595,522 titled “LOCAL TONE MAPPINGFOR SYMBOL READING,” filed Dec. 6, 2017, which is herein incorporated byreference in its entirety.

TECHNICAL FIELD

The techniques described herein relate generally to compressing highdynamic range images for symbol reading, such as for machine visiontechniques used to read symbols.

BACKGROUND

Automated identification and tracking of objects has many applications,for example, in products using optical symbols. Optical symbols arepatterns of elements with different light reflectance or emission,assembled in accordance with some predefined rules. A known optical codeis the linear barcode used in different consumer products. A linearbarcode includes bars or spaces in a linear fashion. A barcode can be,for example, a rectangular identifying symbol that includes one or morespatially contiguous sequences of alternating parallel bars and spaces.Each of the bars and spaces is often referred to as an element. Asequence of one or more contiguous elements makes up an elementsequence. An element in a barcode element sequence can encodeinformation by its relative width. Examples of one-dimensional barcodesthat are known in the art include Code128, UPC, I2of5, Codabar,Pharmacode, Code39, and DataBar symbology types.

Optical codes can also encode information in two dimensions. Atwo-dimensional symbol can include a spatial array of modules or dots.Information is encoded in a two-dimensional symbol according to whetherthe modules are “on” or “off”, or whether dots are present or absent.Examples of two-dimensional symbols that are known in the art includeDataMatrix, QR Code, PDF417, and Maxicode symbology types.

Typically, symbols are created by printing (e.g., with ink) or marking(e.g., by etching) bar elements or modules upon a uniform reflectancesubstrate (e.g. paper or metal). On a paper substrate, the printedelements and modules typically have a lower reflectance than thesubstrate, and therefore appear darker than the unprinted spaces ormodules between them (e.g., as when a symbol is printed on white paperusing black ink). Symbols can also be printed in other manners, such aswhen a symbol is printed on a black object using white paint. Todifferentiate a symbol more readily from the background, it is typicallyplaced relatively distant from other printing or visible structures.Such distance creates a space, often referred to as a quiet zone. For alinear barcode symbol, this quite zone is typically both prior to thefirst bar and after the last bar. For a two-dimensional symbol, thisquite zone is typically on all sides of the symbol. Alternatively, thespaces or “off” modules, and quiet zones can be printed or marked, andthe bars or “on” modules are implicitly formed by the substrate.

The information encoded in a symbol can be decoded using optical readersin fixed-mount installations or in portable hand-held devices. Forexample, in the case of a fixed-mount installation, a transfer linemoves objects marked with symbols in the range of a fixed-mount reader,which can generate images of the symbols. Image-based reader devicestypically include at least one sensor capable of generatingtwo-dimensional images of a field of view (FOV). For example, manysystems currently employ a two-dimensional charge-coupled device (CCD)image sensor, which acquires images that are then received by aprocessor. The processor is programmed to examine image data to identifysymbol candidates and to decode those symbol candidates. Reader devicescan be programmed to obtain images of a field-of-view (FOV) in rapidsuccession and to decode any obtained symbol candidates as quickly aspossible. The processor runs one or more decode algorithms to decode thecode candidates.

Barcode readers generally fall into two categories: laser scanners orimage-based readers. Image based readers are rapidly replacing scannersin a wide range of industries. Image-based methods for locating anddecoding symbol candidates are well known in the art. Examples ofimage-based decoding algorithms include: U.S. Pat. No. 9,607,200,entitled “Decoding barcodes”, U.S. Pat. No. 9,589,199, entitled “Methodsand apparatus for one-dimensional signal extraction”, U.S. Pat. No.9,361,499 “Barcode decoding”, and U.S. Pat. No. 9,218,536 “Methods andapparatus for one-dimensional signal extraction”, which are herebyincorporated by reference herein in their entirety.

When acquiring an image of a symbol, the quality of the image depends onseveral factors, for example, the angle of the reader with respect to asurface on which the symbol is applied, the material and texture of thesurface on which the symbol is applied, the symbol marking quality orany damage occurring after marking, the characteristics (e.g. intensity,wavelengths, direction, etc.) of ambient and any reading devicelighting, any distortion in the applied symbol, the speed the symbol istraveling with respect to the reader (e.g. on a conveyor belt), thedistance of the reader from the surface on which the symbol is applied,any optical blur, the sensor/camera resolution, any sensor noise, anymotion blur (as a result of part motion during sensor exposure), etc.Image quality affects the ability of a processor running a specificalgorithm to decode a symbol. For example, readers often have difficultydecoding some symbols, such as those with poor illumination and/orfeatures that are difficult to identify or distinguish. For example,image-based symbol readers typically require a limited bit depth(usually 8-bit) image with distinguishable symbol details. However, manyimages are captured with poor illumination, and the scene may have ahigh dynamic range (e.g., a very large range of pixel values across theimage that cannot be directly captured directly by a sensor having alimited bit-depth image). In such case, one single image (again,typically 8-bits) may not capture all information needed for symbolreading. One example is the Direct Part Marked (DPM) symbol reading onround shiny metal parts. Symbols, such as DataMatrix codes, printed onsuch samples usually contain both over-exposed and under-exposed regionsdue to strong specular reflections. Therefore, the symbols maybepartially invisible or have poor visual quality in just a single image.For example, bright areas may get clipped uniformly to the highest pixelvalue in order to capture the details of the darker areas, or darkerareas may be clipped uniformly to the smallest pixel value in order tocapture the details of the brighter areas. As another example, forapplications such as parcel handling, a tall object (e.g., a box) may bevery close to the light on the reader so that it may be oversaturated,and a small object may be too far away from the reader so that it isn'tsufficiently illuminated.

In some configurations, such as fixed-mount installations, the opticalreader can obtain a large number of images of the same object andapplied symbol. For example, multiple 8-bit images can be acquired withdifferent gain or exposures and fused to form a composite image.However, image fusion may require registration, buffering images, and/orsignificant processing such that the decoding cannot be performed inreal time.

SUMMARY

The techniques described herein, in some embodiments, compress a highdynamic range (HDR) image into a limited bit-depth (typically 8-bit)image using techniques described herein, which are based in part onlocal features of the pixels. The compressed limited bit-depth imagesare used as input images for the decoders. In some embodiments, therendered limited bit-depth image recovers and retains details from boththe over-exposed and under-exposed regions (e.g., without requiringextra hardware). The decoding rate, decoding speed, and/or the decodingsuccess rate, for example, can be improved by decoding using thegenerated images.

In some aspects, the techniques feature a method for reading a barcodesymbol. The method includes acquiring a raw image of a symbol, whereeach pixel of the raw image has a digital value having a raw bit depth.The method includes identifying a region-of-interest of the raw image;determining a local pixel neighborhood metric for at least one raw pixelin the region-of-interest, wherein the local pixel neighborhood metricidentifies on one or more raw pixels near the at least one raw pixel,and the local pixel neighborhood metric is determined based on at leastone attribute of the symbol in the raw image. The method includesdetermining a local mapping function for the at least one raw pixel thatmaps the value of the raw pixel to a mapped pixel value with a mappedbit depth that is smaller than the bit depth associated with the rawimage, wherein the local mapping function is based on a value of atleast one other raw pixel near the at least one raw pixel within thelocal pixel neighborhood metric, and at least one parameter determinedbased on the raw image. The method includes computing a mapped image forthe region-of-interest, comprising determining a pixel value for atleast one mapped pixel value in the mapped image by applying the localmapping function to the raw pixel value in the raw image to generate themapped pixel value. The method includes decoding the symbol using themapped image.

In some examples, the symbol is a barcode symbol.

In some examples, identifying a region-of-interest includes identifyinga region within the raw image containing the barcode symbol, identifyingthe entire raw image, identifying a predetermined region in the rawimage, or some combination thereof.

In some examples, the method is configured so that the method can beexecuted by an FPGA.

In some examples, the local mapping function depends on pixel values ofall raw pixel values within the local pixel neighborhood metric. In someexamples, the local mapping function is a Reinhard operator.

In some examples, computing the mapped image comprises applying adifferent mapping function to each raw pixel in the region-of-interest.

In some examples, the at least one symbol attribute comprises one ormore of a module size range, a minimum feature size range, a symbolwidth range, or a symbol height range.

In some examples, the at least one symbol attribute is determined basedon a second image of a symbol. In some examples, the second image is aprevious image acquired of the same symbol. In some examples, the secondimage is an image acquired of a different barcode symbol, during atuning phase.

In some examples, the at least one parameter is determined based on asecond image of a symbol. In some examples, the second image is aprevious image acquired of the same symbol. In some examples, the secondimage is an image acquired of a different symbol, during a tuning phase.

In some examples, a global tone mapping technique is applied to a pixelvalue for at least one mapped pixel value in the mapped image byapplying a global mapping function to the raw pixel value in the rawimage to generate the mapped pixel value. In some examples, the globalmapping function, the local mapping function, or both, for the image.

In some aspects, the techniques feature a method for reading a barcodesymbol. The method includes acquiring a raw image of a barcode symbol,where each pixel of the raw image has a digital value having a raw bitdepth. The method includes identifying a region-of-interest of the rawimage. The method includes determining a local pixel neighborhood metricfor at least one raw pixel in the region-of-interest based on anattribute of a symbology of the barcode in the raw image, wherein thelocal pixel neighborhood metric identifies on one or more raw pixelsnear the at least one raw pixel. The method includes determining a localmapping function for the at least one raw pixel that maps the value ofthe raw pixel to a mapped pixel value with a mapped bit depth that issmaller than the bit depth associated with the raw image, wherein thelocal mapping function is based on a value of a set of raw pixels nearthe at least one raw pixel within the local pixel neighborhood metricand at least one parameter determined based on the raw image. The methodincludes computing a mapped image for the region-of-interest, comprisingdetermining a pixel value for at least one mapped pixel value in themapped image by applying the local mapping function to the raw pixelvalue in the raw image to generate the mapped pixel value. The methodincludes decoding the barcode symbol using the mapped image.

In some aspects, the techniques relate to an apparatus for reading abarcode symbol. The apparatus includes a processor in communication withmemory, the processor being configured to execute instructions stored inthe memory that cause the processor to acquire a raw image of a barcodesymbol, where each pixel of the raw image has a digital value having araw bit depth. The processor is configured to execute instructionsstored in the memory that cause the processor to identify aregion-of-interest of the raw image. The processor is configured toexecute instructions stored in the memory that cause the processor todetermine a local pixel neighborhood metric for at least one raw pixelin the region-of-interest based on an attribute of a symbology of thebarcode in the raw image, wherein the local pixel neighborhood metricidentifies on one or more raw pixels near the at least one raw pixel.The processor is configured to execute instructions stored in the memorythat cause the processor to determine a local mapping function for theat least one raw pixel that maps the value of the raw pixel to a mappedpixel value with a mapped bit depth that is smaller than the bit depthassociated with the raw image, wherein the local mapping function isbased on a value of a set of raw pixels near the at least one raw pixelwithin the local pixel neighborhood metric, and at least one parameterdetermined based on the raw image. The processor is configured toexecute instructions stored in the memory that cause the processor tocompute a mapped image for the region-of-interest, comprisingdetermining a pixel value for at least one mapped pixel value in themapped image by applying the local mapping function to the raw pixelvalue in the raw image to generate the mapped pixel value. The processoris configured to execute instructions stored in the memory that causethe processor to decode the barcode symbol using the mapped image.

In some aspects, the techniques relate to at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor to performthe acts of acquiring a raw image of a barcode symbol, where each pixelof the raw image has a digital value having a raw bit depth. Theinstructions cause the at least one computer hardware processor toidentify a region-of-interest of the raw image. The instructions causethe at least one computer hardware processor to determining a localpixel neighborhood metric for at least one raw pixel in theregion-of-interest based on an attribute of a symbology of the barcodein the raw image, wherein the local pixel neighborhood metric identifieson one or more raw pixels near the at least one raw pixel. Theinstructions cause the at least one computer hardware processor todetermining a local mapping function for the at least one raw pixel thatmaps the value of the raw pixel to a mapped pixel value with a mappedbit depth that is smaller than the bit depth associated with the rawimage, wherein the local mapping function is based on a value of a setof raw pixels near the at least one raw pixel within the local pixelneighborhood metric, and at least one parameter determined based on theraw image. The instructions cause the at least one computer hardwareprocessor to computing a mapped image for the region-of-interest,comprising determining a pixel value for at least one mapped pixel valuein the mapped image by applying the local mapping function to the rawpixel value in the raw image to generate the mapped pixel value. Theinstructions cause the at least one computer hardware processor todecoding the barcode symbol using the mapped image.

There has thus been outlined, rather broadly, the features of thedisclosed subject matter in order that the detailed description thereofthat follows may be better understood, and in order that the presentcontribution to the art may be better appreciated. There are, of course,additional features of the disclosed subject matter that will bedescribed hereinafter and which will form the subject matter of theclaims appended hereto. It is to be understood that the phraseology andterminology employed herein are for the purpose of description andshould not be regarded as limiting.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 shows an exemplary image processing system, in accordance withsome embodiments;

FIG. 2 shows a global tone mapping technique, according to someexamples;

FIG. 3 shows a flow chart of an exemplary computer process for usinglocal tone mapping to compress a high dynamic range image, according tosome embodiments;

FIGS. 4A-4B show the effect that a Gaussian kernel size can have on theimage processing, according to some examples;

FIGS. 5A-5C show the 4-11 bits, 3-10 bits and 2-9 bits, respectively, ina 12 bit image, according to some examples;

FIG. 6 shows an example of an HDR image that is locally tone mappedaccording to the techniques described herein, according to someembodiments;

FIG. 7 shows an example of two 8-bit images which are the mostsignificant 8-bits and the least significant 8-bits of an acquired12-bit HDR image and the 8-bit image created from the same HDR imageusing the local tone mapping techniques discussed herein, according tosome embodiments;

FIG. 8 compares images created using a global tone mapping technique toimages created using the local tone mapping techniques disclosed herein,according to some embodiments;

FIGS. 9A-9B compare an 8-bit image (most significant 8-bits of a 12-bitHDR image) to a locally tone-mapped image (from the same 12-bit HDRimage), according to some embodiments;

FIG. 10 is a graph showing an example of how the scene key can affectthe pixel mapping from an HDR image to a lower bit depth image,according to some embodiments;

FIG. 11 is an image of a 2D barcode that illustrates exemplary edges atwhich a large kernel size may cause halos, according to some examples;

FIG. 12 includes images of a 2D barcode that illustrates exemplary edgeswith halos, according to some examples;

FIG. 13 includes images of a 2D barcode that illustrate exemplarycapping and stretching, according to some embodiments;

FIG. 14 is a flow chart of an exemplary computer process for using localtone mapping to compress a high dynamic range image, according to someembodiments;

FIG. 15 shows a graph of an exemplary weighted histogram used toillustrate an intensity calculation, according to some embodiments;

FIG. 16 shows a graph of an exemplary histogram used to illustrate aweighted local histogram calculation, according to some embodiments;

FIG. 17 is a graph showing an exemplary mapping of 12 bit values to 8bit values, according to some embodiments;

FIG. 18 shows exemplary images processed using various tone mappingtechniques, according to some embodiments; and

FIG. 19 shows images to illustrate an example of excluding one or moreportions of the HDR image from processing, according to someembodiments.

DETAILED DESCRIPTION

The inventors have recognized and appreciated that, contrary to typicalimage processing techniques that often use higher-bit depth images(e.g., high dynamic range (HDR) images) when available, better symboldecoding can be achieved by locally tone-mapping HDR images to createlower-bit images for symbol decoding. Basic techniques to reduce ahigh-bit HDR image to a lower-bit image for processing can include alinear mapping that ignores the least significant bit(s) (e.g., the fourleast significant bits) of the HDR image to produce the lower-bit image.However, the inventors have determined that such basic techniques canresult in high data loss with minimal benefit (e.g., with little or nosignal-to-noise improvement compared to the original HDR image). Globaltone mapping often involves processing each pixel in the HDR image inthe same manner to generate the lower-bit image. The inventors havedetermined that global tone mapping can have a moderately improvedbenefit (e.g., compared to a linear mapping), but can still result insignificant data loss.

The inventors have developed local tone mapping techniques toindividually process each pixel in the HDR image to generate a lower-bitimage. The techniques can reduce data loss, can have substantialbenefits compared to other techniques, or both. In some embodiments, thelocal tone mapping techniques can map each pixel relative to itsneighborhood intensity. Such local tone mapping can reduce data loss(e.g., compared to global or linear tone mapping techniques) and have asubstantial benefit compared to other techniques, as discussed herein.In some embodiments, the local tone mapping techniques can map eachpixel relative to both its neighborhood intensity and contrast. Theseand other techniques discussed further herein can enhance contrast invarious regions of the image, which can improve image processingtechniques (e.g., symbol decoding algorithms). The local tone mappingtechniques, in some embodiments, only require a single HDR image, ratherthan needing to fuse multiple images.

In some embodiments, rather than compressing HDR images in a manner thatavoids image processing artifacts that may not be aesthetically pleasingto a human observer, the local operator is configured to benefit adecoder. An example of a characteristic that might be unpleasant forhuman eyes, but not for decoders, can include extreme contrastenhancement near edges (e.g., halo artifacts). Such extreme contrast, ingeneral, may not affect decoding performance because it is a contrastenhancement, and can therefore be beneficial for decoding. Anotherexample of a characteristic that might be unpleasant for human eyes isoverall contrast compression. For example, while the techniquesdiscussed herein may enhance the local contrast by considering localneighboring pixels, larger bit information (e.g., 10/12/16 bitinformation) is compressed to smaller-bit information (e.g., 8-bitinformation). So in some regions, human eyes may perceive the portion ofthe image to be “grayed out.” However, the decoder can be configured tohandle (very) low contrast symbols. A further example of acharacteristic that might be unpleasant for human eyes is performing thetechniques discussed herein on a (e.g., user selected)region-of-interest (ROI). If the processing is performed on theinformation inside the ROI, the textures or details out of the ROI maybe, e.g., over-saturated or under-saturated, but it may not affect thedecoder because the decoder may not be processing such regions. Theinventors have further appreciated that image processing techniques(e.g., to decode symbols) used for machine vision can be performed basedon different parameters, such as the image, symbol, and/or environment.For example, the techniques can be configured to result in a successfuldecoding, even though the image processing does not take into account(or worsens) traditional aspects maintained for human observers.

In the following description, numerous specific details are set forthregarding the systems and methods of the disclosed subject matter andthe environment in which such systems and methods may operate, etc., inorder to provide a thorough understanding of the disclosed subjectmatter. In addition, it will be understood that the examples providedbelow are exemplary, and that it is contemplated that there are othersystems and methods that are within the scope of the disclosed subjectmatter.

Some of the illustrative examples provided herein describe thetechniques in the context of HDR images with symbols (e.g., linear ortwo-dimensional barcodes). These examples are for exemplary purposesonly and are not intended to be limiting. It should be appreciated thatthe techniques can be used for other general inspection applicationsthat do not analyze symbols. For example, the techniques can be used forbinary image applications, e.g., where the system is inspecting afeature that is dark on light (or vice versus). Such techniques can beconfigured based on the feature size of interest (e.g., the minimumand/or maximum feature size), which can be used to set up the parametersdiscussed further herein.

Referring to FIG. 1, aspects of the techniques discussed herein will bedescribed in the context of an exemplary imaging system/transfer line 10wherein a transfer line 30 moves objects 26 a, 26 b, 26 c, etc., along adirection of travel 25. A person of ordinary skill would understand thatthe imaging system 10 is an exemplary fixed-mount application and thatthe disclosed systems and methods for reading symbols on objects areapplicable in other applications, including hand-held applications.

In the present example, each of the objects has similar physicalcharacteristics and therefore, only one object, for example, object 26b, will be described in detail. Specifically, object 26 b includes asurface 27 which faces generally upward as object 26 b is moved bytransfer line 30. A symbol 24 a is applied to top surface 27 foridentification purposes. Similar-type symbols 24 a are applied to topsurfaces of each of objects 26 a and 26 c.

The image processing system includes a sensor 22 including optics 24that define a field of view 28 below the sensor 22 through whichtransfer line 30 moves the objects 26 a, 26 b, 26 c, etc. Thus, as theobjects move along direction of travel 25, each of the top surfaces 27comes into field of view 28. Field of view 28 can be large enough suchthat the entire top surface 27 is located at one point or another withinthe field of view 28 and therefore any code applied to the top surface27 of an object passes through the field of view 28 and can be capturedin an image by sensor 22. As the objects move along the direction oftravel 25, the sensor 22 can capture partial fragments of the symbol 24a applied to the top surface 27. A person of ordinary skill wouldunderstand that the field of view can be large enough that it cancapture more than one object. Moreover, the image processing system 10can include more than one sensor and/or a sensor with a field-of-viewexpander as described in U.S. Pat. No. 8,646,690 entitled “System andMethod for Expansion of Field of View in a Vision System,” to Nunnink etal., the contents of which are incorporated herein in their entirety.

The image processing system 10 also includes a computer or processor 14(or multiple processors) which receives images from sensor 22, examinesthe images to identify sub-portions of the images that may include aninstance of a symbol as symbol candidates and then attempts to decodeeach symbol candidate in an effort to identify the object currentlywithin the field of view 28. To this end, sensor 22 is linked toprocessor 14. An interface device 16/18 can also be linked to processor14 to provide visual and audio output to a system user as well as forthe user to provide input to control the imaging system, set imagingsystem operating parameters, trouble shoot imaging system problems, etc.A person of skill in the art will appreciate that while the sensor 22 isshown separate from the processor 14, the processor 14 can beincorporated into the sensor 22, and/or certain processing can bedistributed between the sensor 22 and the processor 14. In at least someembodiments, the imaging system also includes a tachometer (encoder) 33positioned adjacent transfer line 30 which may be used to identifydirection of travel 25 and/or the speed at which transfer line 30transfers objects through the field of view.

In some embodiments, the sensor 22 and processor 14 are a Cognex DataManand/or Cognex MX series of industrial, image-based barcode readers thatscan and read various symbols, such as 1D and 2D codes. In someembodiments, the image processing techniques discussed herein areexecuted on a system embedded in the sensor 22, which includes dedicatedmemory and computational resources to perform the image processing(e.g., to perform the local tone mapping techniques discussed herein).In some examples, a single package houses a sensor (e.g., an imager) andat least one processor (e.g., a programmable processor, a fieldprogrammable gate array (FPGA), a digital signal processor (DSP), an ARMprocessor, and/or the like) configured to perform image processing.

Some sensors provide the option of acquiring high dynamic range images,by using more bits for each pixel (e.g. 12 or 16 bits per pixel,compared to 8 bits, or less). However, it may not always be beneficialto use such high dynamic range images for machine vision techniques. Forexample, depending on the system configuration there may be limitationson memory usage and computational or processing power. As anotherexample, there are practical software limitations, such as data imagestructure design limitations, or high bit-depth support limitationsimposed by image analysis tools employed to locate and decode thesymbols. For example, it can take much more time and more arithmeticunits to do 16-bit operations than 8-bit operations, and also typicallyrequires much more memory to store the internal results. As anotherexample, display devices may only support 8-bit display, and thereforeit may be difficult to understand the decoding results if the image seenby the user through the display is different than the images used bydecoder for processing. Therefore, in some embodiments, such highdynamic range images are not analyzed directly by symbol decodingalgorithms, rather the high dynamic range images are compressed into8-bit images prior to processing using local tone mapping techniques.

In some situations, the information needed to properly read a symbol hasbeen captured in a single HDR image (e.g., such that multiple images arenot needed). The techniques disclosed herein provide for extractinguseful information on the dark and bright regions from the HDR image andcombining the extracted information into one limited bit-depth (e.g.8-bit) image. The limited bit-depth image can be used for decoding,thereby achieving decoding in a manner that is locally adjusted for eachimage and/or area of interest in the image.

Some techniques for tone mapping can include a global tone mapping thatis performed in the same manner for each pixel of each image, forexample by applying the same pixel mapping function to each pixel. Suchtechniques are known in the art, and are sometimes a configurable optionof an imaging sensor. Various global mapping functions can be used, suchas for the purpose of enhancing the contrast for dark regions, forenhancing the contrast for bright regions, and/or for the purpose ofenhancing intermediate the contrast of regions over some range ofbrightness. However, such global mappings are rarely able to optimizecontrast for the entire range of a high dynamic range image. FIG. 2shows an example of piece-wise linear tone mapping, according to someexamples. This illustrative global tone mapping curve can be used toenhance details in dark region. However, it has some limitations: 1)darker pixels can be driven to grayer values, 2) bright details in animage can be minimized, and 3) darker background textures can beproduced. Pixels associated with each of these limitations areillustrated in the sample image 210: reference character 1 representsdarker pixels that can be driven to grayer values (limitation 1),reference character 2 represents brighter details in an image that canbe minimized (limitation 2), and reference character 3 represents darkerbackground textures that can be enhanced (limitation 3). Thesecomponents result in the curve 220, which is used to map the 12-bit rawpixel values (x axis, 230) to 8-bit tone mapped pixel values (y axis,240). The curve 220 graphically illustrates the three regions, namelyenhancing dark pixels 220A (region 1), compressing bright pixels 220B(region 2), and saturating pixels 220C (region 3). Thus, for piece-wiselinear tone mapping, this three-component technique as illustrated incurve 220 is applied to every pixel in the image.

Global tone mapping techniques are typically fixed after an initialoff-line tuning process, and therefore are not tailored to each imageand/or region(s) of interest in the image. Even if the global tonemapping curve were configured per image, it would still be limited toenhancing the contrast of a limited range of raw (original) pixelvalues. Global tone mapping techniques may not work well on certainimages, such as those subject to extreme light or when a sample positionin the image changes after tuning. Global tone mapping techniques mayalso lose bright details, such as when driving dark pixels to graypixels. Global tone mapping may enhance dark background textures, whichcan slow down symbol reading.

The local tone mapping techniques discussed herein can configure thepixel processing on a per-image and/or per-pixel basis. Local tonemapping can achieve better symbol decoding compared to fused images andglobal tone mapping, as discussed further herein. For example, localtone mapping can increase the dynamic range of pixels and pixel contrastacross the image, e.g., compared to global tone mapping techniques. Asanother example, local tone mapping can further adjust pixel compressionfor various local regions throughout the image. As a further example,local tone mapping can enhance contrast for both dark and light regions.

FIG. 3 shows a flow chart of an exemplary computer process 300 usinglocal tone mapping to compress a high dynamic range image, according tosome embodiments. At step 302, the imager acquires a high dynamic rangeimage. For example, as discussed above some sensors have the capabilityof acquiring images with a bit-depth more than 8-bits per pixel (e.g.,10-bits or 12-bits per pixel, or more).

At step 304, the processing unit associated with the imager (e.g., anARM processor, or a FPGA incorporated into the same housing as theimager) determines parameters for image processing. The processing unitcan determine the parameters using one or more techniques. For example,the parameters can be computed based on the real values from the currentacquired image. As another example, the parameters can be an estimatefrom previously acquired image (e.g., that can be refined for thecurrent image). Using the previous parameters as an estimate can helpimprove and/or speed up processing of new images. As a further example,the parameters can be configured during a configuration process. Forexample, the parameters can be determined empirically and tested over aset of testing images to ensure the parameters achieve a high decodingrate.

The parameters can include, for example, a scene key, a minimum pixelvalue, and/or a maximum pixel value. The scene key can be computed byaveraging pixel intensity values of the image of interest, such asaveraging all pixels in the image, and/or averaging the pixel intensityvalues of one or more regions of interest (ROI) in the image (e.g., aregion determined to likely include the symbol to decode, or a regionselected by a system operator). In some embodiments, the scene key canbe the same or adjusted scene key computed in a previous ROI of an imageanalyzed in the previous run. For example, if the processing system doesnot have any knowledge of the current image, the system can use theaverage intensity of the entire image. In some embodiments, such as whenthe system may have knowledge of where the symbols are placed (e.g., inthe center of the field of view if an aimer is used, or if tuningreturns a rough location of the symbol, and/or from performing a texturebased analysis), the processing system can use the average intensity inthat smaller region of interest (e.g., so the distracting backgroundregions has less affect).

In some examples, the min/max values may be the real maximum/minimumvalues from the current image, and/or a maximum/minimum portion (e.g.5%) of an image. As another example, the min/max values can be anestimation from a previous image. As a further example, that min/maxvalues can be pre-configured based on the illumination and exposure/gainvalues used in image capturing.

At step 306, the processing device determines surrounding informationfor each pixel. For example, the processing device can apply one or moretechniques to analyze neighboring pixels. The local neighboring pixelinformation is used to adjust the center pixel intensity while enhancingthe local contrast. In some examples, the processing device uses aGaussian kernel to analyze neighboring pixels. The processing device canbe configured to determine the particular size (e.g., radius) to use forthe Gaussian kernel to calculate the intensity of each pixel'ssurrounding pixels.

The size of the Gaussian kernel can have an effect on properly decodingthe symbol. For example, the size may need to be large enough to enhancethe local contrast, and/or small enough to avoid creating artifacts thatwill degrade decoding (e.g., artifacts that might cause the decoder tothink there is an edge when there is not an edge). As another example,the size of the kernel may be configured based on symbol attributesand/or characteristics. For example, the symbol attributes and/orcharacteristics can include one or more of a minimum, a maximum, and/ora range of the module size, the minimum feature size, the symbol width,the symbol height, and/or the like.

FIGS. 4A-4B show the effect that a Gaussian kernel size can have on theimage processing, according to some examples. FIG. 4A shows that if thekernel size is too small, then each module may not provide sufficientinformation from neighboring pixels, resulting in insufficient contrastenhancement. FIG. 4B shows that if the kernel size is too large, it maycause artifacts such as a black halo on each side of white areas (e.g.,the black halo 420, 422 on each side of white area 424). A person ofskill will appreciate that these images and other images discussedherein are for exemplary purposes, and are not intended to be limiting.

The processing device can therefore determine an appropriate number ofneighboring pixels to analyze to set the Gaussian kernel size. Forexample, the processing device can determine the Gaussian kernel sizebased on the symbol and/or characteristics of the image. For example,the processing device can determine the kernel size based on how manymodules or elements are in the symbol, how big the modules or elementsare, and/or the type of symbol (e.g., 1D or 2D symbol). As anotherexample, even if a decoding effort is not successful, it may generatesome information about the symbol or the current image. For example, thedecoder may determine how big (e.g., in terms of pixels) each module is,so the decoder can adjust the size of the kernel according to the modulesize. As another example, the decoder can determine the contrast at thesymbol region. For example, if the contrast is very low (e.g., a darksymbol on a darker background, or a bright symbol on a whitebackground), the decoder can use a larger kernel, e.g., because usinglarge kernel may introduce stronger contrast enhancement.

In some embodiments, the processing device can iteratively try differentkernel sizes depending on the result of the decoding. For example, ifthe decoding is unsuccessful but obtains symbol information during theattempt, the processing device can re-calculate the kernel size byvarying the parameter based on the symbol and/or the image as discussedabove.

In some embodiments, the kernel size can be determined based onpre-configured tuning and/or a previously processed image. As anotherexample, the kernel size can be determined by testing the techniquesover a training database to determine likelihood of a successfuldecoding for the particular kernel size, including based on theparticular objects under investigation, the imaging set-up and/or theimaging environment.

In some embodiments, the kernel size is known to create artifacts. Asdiscussed herein, the size of the Gaussian kernel need not be selectedin a manner that avoids artifacts for purposes of human viewing (e.g.,such that the image is processed in a visually aesthetic manner). Forexample, halos may be an acceptable processing result for the image ifit results in the symbol being properly decoded. A large kernel size maycause halos across strong edges, such as the exemplary edges 1102, 1104shown in FIG. 11, according to some examples. In some embodiments thebest neighboring region (e.g., kernel size) should be the largest areain which no significant contrast changes occur, and that kernel sizeshould be determined per pixel based on each pixel's unique localfeatures. In some embodiments, rather than determining a per-pixelkernel size, the techniques can use one fixed-size kernel for multiplepixels, and/or all pixels in the entire image. The size of the kernelcan still be configurable per image.

As discussed above, a halo artifact is an example of an artifact thatcan be caused by using one fixed size local kernel, which often resultsin a strong contrast enhancement at the edges (e.g., for high-contrastsymbols). In some embodiments, decoders are configured to decode symbolsif the minimum feature size (e.g., the module size or narrowest barwidth, measured in ppm, or pixels per module) is within a particularrange. For symbols that are too large or too small, the system may firstrescale the symbol to the right size so it is in the range. Therefore,the kernel size can be determined based on the minimum feature size,such as an upper bound of the range. Determining the kernel size basedon the minimum feature size can enhance the local contrast for differentsized symbols. For example, using a large kernel on a small code maycause halo artifacts, but such halo artifacts may not harm the decodingprocess because the halo is, in a sense, an extreme contrast enhancementthat may not affect decoding. For example, the size of the kernel can bedetermined based on the upper-bound of the minimum feature size ofsymbols that are (or will be) seen by the decoder. While using thelarger kernel (e.g., even if a smaller kernel may suffice) may requireadditional computation time (e.g., because it needs to be convolved withevery pixel), the kernel size and/or coefficients can be optimized toimprove execution speed.

As an example, halo artifacts may be seen when the center and itssurrounding intensity are very different, such that halo artifacts areseen near high contrast edges. FIG. 12 includes images 1200 and 1250 ofa 2D barcode that illustrates exemplary edges with halos, according tosome examples. Referring to image 1200, human eyes can see brighterbands near the circled edges, but such brighter bands may not cause anyextra detected edges and/or difficulty determining whether a moduleshould be classified as a white or black module. Referring to image1250, if the module is small, the halos from different features mayconnect with each other, as shown via the circles. Such connected haloscan behave like a strong contrast enhancement (e.g. the original (raw)intensity of the white module was mapped to gray as shown in circle1252, but was mapped to white as shown in the circle 1254. Such a strongcontrast enhancement can benefit the decoder, in most cases.

Other artifacts that can be acceptable include contrast reduction. Thetone mapping algorithm can capture the details of a high bit-depth (e.g.12 or 16 bit) image into a single 8-bit image, so more these details canbe seen (such as the area marked with the circles 502, 602 in FIGS. 5A,6, which is barely visible in the original top image as shown in 502 butmuch clearer in the local tone mapped image as shown in 602). Butdepending on the parameters, some regions may have lower contrast thanthe original (see, e.g., the area marked by circles 504, 604 in the twoimages 5A, 6). Such lower contrast is not typically a problem fordecoders that can handle low contrast codes. Additionally, theparameters can be adjusted as described herein in order to minimized anyeffects on decoding.

During run-time image acquisition, compression and/or decoding, theprocessing device can further configure the corresponding kernel sizebased on the minimum feature size. For example, while the currentconfigured kernel size is large, for a smaller code, the system mayselect a smaller kernel (e.g., to further conserve computation and/ormemory resources). The system may do so if the system has knowledge ofthe size of the symbol minimum feature size (e.g., from off-line tuning,training, or manual user input).

A person of skill in the art will appreciate that steps 304 and 306 canbe performed separately, and/or in combination. As an illustrativeexample, the any number of parameters discussed in conjunction withsteps 304 and 306 can be configured off-line, e.g., prior to step 302.For example, a range of different tone mapping parameter settings (e.g.,including kernel size) can be tested. Each setting can be tested byattempting to decode a set of images (e.g., stored in a large imagedatabase) that are representative of a range of symbol attributes(and/or other conditions, such as lighting conditions) for symbols thatneed to be read. The parameter setting that optimizes decodingperformance (maximizes the number of symbols read correctly, andminimizes misreads) over the image set can be selected for use with theimager or reader. Symbol attributes over which images would need to becaptured might include, for example, minimum feature size, module size,background texture, symbol size, symbology type, and/or the like.

As another illustrative example of off-line tuning, the best parametersettings can be determined for sub-ranges of symbol attributes, ratherthan determining just a single set of parameters that optimizes theoverall performance as discussed above. In some embodiments, atrun-time, if any of the attributes are known, then a more optimalparameter setting can be tailored to the attributes of the particularimaging environment. For example, parameters may be known because theoperator sets them manually at the customer site (e.g., the symboltype), because they are learned or trained by example (e.g. the modulesize), and/or the like.

In another illustrative example, the parameters can be tuned for aparticular application. This can be accomplished similarly toperformance tuning discussed above, but performed for the specificdeployment. For example, instead of using an image database, the readercan first acquire images automatically, and systematically change thelighting and other acquisition settings such as exposure and gain. Suchtechniques can be used to optimize the tone mapping parameter settings,including while acquiring images of possibly more than one symbol.

If only a single symbol is used for tuning, the symbol can be decodedand the measured attributes (e.g. module size or symbol size) can beused to choose optimal tone mapping parameters. Even if more than onesymbol were employed, the range of possible attributes (e.g. the rangeof module sizes) can be used to choose the parameters. The determinationcan leverage a heuristic analysis (as discussed further below), and/oruse the best parameter settings for the appropriate sub-range ofsettings that may have been pre-determined as discussed above.

As another illustrative example, the settings can be based on apreviously compressed and/or decoded image. Such processing can beperformed on-line, between images, e.g., in the manner discussed abovein the previous example. In some examples, if the image is the firstimage of a sequence, default parameter values can be used for the firstimage. For example, if the system can track the symbol, then the systemcan incorporate knowledge of whether or not the system is looking at anew symbol. For example, the tracking techniques disclosed in U.S.patent application Ser. No. 14/510,689, entitled “Systems and Methodsfor Tracking Optical Codes,” can be used to track the symbol, which ishereby incorporated by reference herein in its entirety.

The examples and techniques discussed above may not be mutuallyexclusive. For example, a manual tuning method can be used to determinesettings that are employed for the “default” tone mapping parameters,and a run-time tuning step can be used to refine the settings. Asanother example, the previous image can be used to override the defaultparameters, e.g., if we know we are tracking the same symbol over timesuch that leveraging parameters determined for the previous image wouldapply for the current image.

At step 308, the processing device optionally adjusts (e.g., caps) thecenter-surrounding ratio to, e.g., to avoid artifacts that may affectthe decoding process. In some embodiments, a maximum threshold can beconfigured to adjust center-surrounding ratios to ensure ratios arebelow the threshold. For example, the center-surrounding pixel intensityratio can be capped to a certain ratio to avoid the halo artifactscaused by sudden intensity changes in neighboring regions if such haloartifacts will affect decoding. The threshold can be determined in asimilar manner as the parameters discussed in conjunction with steps 304and 306, e.g., off-line as part of a tuning or training step.

As discussed herein, visual artifacts (e.g., halos), in general may notaffect decoding results, so the cap discussed in conjunction with step308 and/or the stretch discussed in conjunction with step 312, areoptional. In some embodiments, the images are not only used as the inputfor the decoders, but may also sometimes provide visual feedback forusers (for example, on a SDK). Additionally, in some implementationsvisual artifacts may affect decoding, e.g., depending on the decodingmethod used. It can therefore be desirable to make the images visuallypleasant.

FIG. 13 includes images 1300, 1320, 1340, 1360 of a 2D barcode thatillustrate exemplary capping and stretching, according to someembodiments. Image 1300 shows an example of a center-surrounding tonemapping only, which includes black and white halos near edges as shownin 1302, 1304. Image 1320 shows an example of a center-surrounding tonemapping with a cap. Image 1320 demonstrates that less of a halo effectis seen near the same location as that of 1302, 1304 in image 1300(marked with 1322 and 1324 in image 1320) due to capping thecenter-surrounding differences. However, the contrast of image 1320 islower than image 1300. Images 1340 and 1360 are discussed further belowin conjunction with step 312.

As another example, referring to FIG. 10, the graph 1000 shows (usingjust a non-linear mapping using the scene key, and the size of theGaussian kernel=1 for exemplary purposes only) that a 12-bit pixel isguaranteed to be mapped to a value within the range of 0-255, but themaximum value does not necessary reach 255. In some examples, if themaximum value does not reach 255, then the image may appear washed out.The mapped pixels can be adjusted (e.g., linearly stretched) to maximizethe dynamic range of the tone mapped image to fully utilized the 8-bit(0-255) intensity differences.

In some embodiments, the minimum and maximum values can be determinedfor specific regions in the image. For example, the system can determinethe maximum and minimum values at the symbol region (e.g., symbolcontrast may be obtained from previous image or previous decodingefforts). The system can maximize the contrast using the maximum andminimum values just in that region, without considering the otherbackground regions in the image as a whole.

At step 310, the processing device performs local tone mapping based onthe parameters determined in the previous steps (e.g., the parametersdetermined at steps 304 and 306). The tone mapping compresses a highdynamic range image (e.g., an HDR image) to a lower-dynamic range image,such as by compressing a 10 or 12-bit HDR image to an 8-bit image.

For example, the following mapping equation can be used to apply localtone mapping:

$\begin{matrix}{{ToneMappedPixel} = \frac{CenterPixelIntensity}{{SceneKey} + {SurroundingIntensity}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

where:

CenterPixel Intensity is the particular pixel intensity value;

SceneKey is the scene key calculated for the image (e.g., by averagingpixels in the image); and

SurroundingIntensity is calculated for the pixel based on neighboringpixels (e.g., using a Gaussian kernel).

Referring to the SceneKey, as discussed above, the scene key can becomputed based on the average intensity of the entire image or theregion of interest (and/or may be obtained from the present image or aprevious image). FIG. 10 is a graph 1000 showing an example of how thescene key can affect the pixel mapping from an HDR image to alower-pixel image, according to some embodiments. The x-axis of graph1000 is 12-bit pixel values, and the y-axis of the graph 1000 is 8-bitpixel values. As shown in the graph 1000, the scene key controls theshape of the non-linear mapping curve, which controls how pixels aremapped from a 12-bit pixel to an 8-bit pixel. A smaller the key value isshown by the first red curve 1002, with the key value=200, whichindicates the original image is relatively dark therefore need moreenhancement in the dark regions. Because the curve 1002 is higher thanthe other curves 1004 (with a key value of 750) and 1006 (with a keyvalue of 2000), the overall intensity of pixels using the scene key of200 is increased more compared to other curves 1004, 1006.

In some embodiments, a different mapping equation is applied to eachpixel. For example, the CenterPixelIntensity and/or theSurroundingIntensity may differ among the various pixels, resulting in adifferent mapping equation used for each pixel.

At step 312, the processing device can be configured to adjust the pixelintensity values calculated in step 310. For example, the processingdevice can be configured to stretch the intensity of the tone mappingimage to fully utilize the 8-bit dynamic range for the resultingcompressed image. In some embodiments, the processing device can performa linear stretch using the minimum and maximum pixel values determinedin step 304.

Referring further to FIG. 13, image 1340 shows an example of acenter-surrounding tone mapping with a pixel stretch. The linear stretchis added, e.g., to fully utilize the 8-bit dynamic range. Image 1340results in a better global contrast, e.g., compared to image 1300, butcan suffer from more severe halos. Image 1360 shows an example of acenter-surrounding tone mapping, a cap (as discussed in conjunction withstep 308), and a stretch. The result of image 1360 is pleasant for humanvision and only includes minor visibal halos that are unlikely to bebothersome to a human observer.

Thus, the techniques discussed herein provide for a local tone mappingtechnique that combines large scale aspects of the image (e.g., adjustedby the scene key), as well as local aspects specific to each tonemapping, adjusted by the surrounding pixel intensity. Such techniquesare computationally efficient, and can therefore be executed by embeddedsystems (e.g., by avoiding intensive computation, such as log operatorsand/or multi-scale kernels). Therefore, the techniques are suitable torun on variant platforms, such as FPGAs, DSPs, ARM processors, GPUs,and/or other dedicated chips. In some embodiments, the techniques caninclude a Reinhard operator, such as discussed in E. Reinhard, M. Stark,P. Shirley, and J. Ferwerda, ‘Photographic Tone Reproduction for DigitalImages’ In ACM Transactions on Graphics, 2002, which is herebyincorporated by reference herein in its entirety.

FIGS. 5A-5C show the 4-11 bits, 3-10 bits and 2-9 bits, respectively, ina 12 bit image which contains 9 codes in the field of view, according tosome examples. When processing the image in FIG. 5A, the system can onlydecode the upper left two codes and the center right code. Whenprocessing the image in FIG. 5B, the system can decode more symbols butthe code in the upper right corner is completely washed out. Whenprocessing the image in FIG. 5C, the originally dark codes in FIG. 5Acan be seen but more symbols are saturated. Therefore, all theinformation needed for the system to decode both symbols is captured inthe original 12-bit image, but not in any 8-bit subset of this image.FIG. 6 shows an example of an HDR image that is locally tone mapped fromthis 12-bit image according to the techniques described herein,according to some embodiments. When processing the image in FIG. 6, thesystem can decode all codes using only one captured image.

FIG. 7 shows another example of the most significant 8-bits of a 12-bitHDR image 700, and the least significant 8-bits of the same 12-bit HDRimage 720 of a symbol, neither of which result in a symbol read. Theimage 750 created from this 12-bit HDR image using the local tonemapping techniques discussed herein allowed the system to decode (orread) the symbol.

FIG. 8 compares images created using a global tone mapping technique toimages created using the local tone mapping techniques disclosed herein,according to some embodiments. The piece-wise linear global tone mappingis not optimized locally for the symbols, and therefore the resultingimages 820, 822 do not result in clear symbols compared to when usinglocal tone mapping, which creates images 840, 842 that enhance thecontrast in both the dark and light regions. Images 820 and 840 showexamples of multiple codes located in dark and bright regions in thefield of view, respectively. Images 822 and 842 show examples of onecode that has half dark and half bright regions.

FIGS. 9A-9B compare the most significant 8-bits of a 12-bit HDR image(9A) to a locally tone-mapped image computed from the same 12-bit HDRimage (9B). The 8-bit image 9A did not result in a symbol read, whilethe tone mapped image 9B resulted in a read. In some embodiments, thedecoder is able to decode the tone mapped image (e.g., 9B) faster thanthe 8-bit image (e.g., 9A), and/or more symbologies may be visible,depending on the number of symbologies in the image.

Depending on the characteristics of the HDR image, such as an image withdark regions, the local tone mapping techniques can enhance details insuch darker regions. The inventors have also discovered and developed acontrast-based local tone mapping technique, which maps each pixelrelative to both its neighborhood intensity and contrast. Thecontrast-based techniques can be used to improve data loss (e.g.,depending on the acquired images, symbology, etc.), to enhanceover-exposed (e.g., bright) regions in addition to dark regions, and/orthe like. For example for images with both dark and bright regions,contrast-based local tone mapping techniques can enhance details in bothdark and light areas (e.g., regardless of image intensity).

FIG. 14 is a flow chart of an exemplary computer process for using localtone mapping to compress a high dynamic range image, according to someembodiments. At step 1402, the imager acquires an HDR image (e.g., animage with 10, 12 or 16 bits, as discussed in conjunction with step 302in FIG. 3). At step 1404, the processing device determines a region ofinterest, such as one or more ROIs likely to contain one or more symbols(e.g., barcodes).

At step 1406, the processing device determines one or more attributes ofthe symbology of the symbol in the raw image. The attribute can include,for example, a minimum size, a maximum size, or both (e.g., a range), ofthe attribute. For example, the attribute can include an expected rangeof possible barcode resolutions (e.g., determined based on a maximum andminimum PPM). The PPM can measure, for example, aspects of the modulesize, the feature size, a width and/or height of a symbol, and/or otheraspects of the symbology.

At step 1408, the processing device determines surrounding informationfor each pixel. For example, the device can use the one or moreattributes determined in step 1406 to determine a number of neighboringpixels for performing local tone mapping. In some embodiments, thesystem can use, for example, a barcode resolution range to determine aGaussian neighborhood size (e.g., sigma). For example, the system candetermine a Gaussian kernel size that spans a sufficient number ofpixels so that the kernel includes at least a few modules in eachdimension of the pixel that the device is processing.

In some embodiments, the techniques determine information for each pixelin the region (e.g., in the Gaussian kernel). For example, in someembodiments the processing device determines for each pixel in theregion with coordinates (i,j) the intensity (intensity(i,j)) and thecontrast (contrast(i,j)). The processing device can determine theintensity using, for example, an average of local pixels, a median, oran average of local maximum and minimum values. The processing devicecan determine the contrast using, for example, a standard deviation, ahistogram, and/or the like. In some embodiments, the processing devicecan determine weighted metrics. For example, the processing device candetermine a weighted average and a weighted standard deviation of thepixel intensities in the local neighborhood. The weights can bedetermined, for example, by a Gaussian profile. For example, ifG(x,sigma) is a 1D Gaussian profile of a Gaussian with width σ (standarddeviation), and x is the distance from the center of the profile, thenthe weight for a pixel can be G(d, σ), where d is the distance of thepixel from the neighborhood center. In some embodiments, G(x, σ) can benormalized so that the sum off the weights for the local neighborhoodadd to one. For example, G(x)=e^(−(x̂2)/(2×σ̂2))/N, where N is thenormalization factor that makes 1=Σ_(x=0) ^(n)G(x). In some embodiments,G can reflect or be used to determine the local neighborhood. Forexample, the neighborhood boundary can be determined based on d=3×σ (3standard deviations). In some embodiments, if the size of a kernel isvery large, the Gaussian kernel may be smooth, so a uniform weight canbe used to speed up processing. In some embodiments, the Gaussian kernelcan be a 2D kernel (e.g., N×N) or a 1D kernel (e.g., if the direction ofsymbol of interest is known). For example, a 1×N kernel can be used ifall symbols are horizontal.

In some embodiments, the processing system can limit the informationdetermined for the pixels. For example, the processing system can limit(e.g., clamp) the contrast to prevent the contrast from going aboveand/or below a certain value or range. The processing system can beconfigured to limit the contrast to a certain value or range, and/or candetermine an optimal limit during training or testing, as discussedherein. Limiting the contrast can, for example, help avoid amplifyingnoise when the signal is not bimodal. For example, in some embodimentsas discussed further below, it is desirable to process signals that arebetween y1 and y2 shown and discussed in conjunction with FIG. 17, sincethe pixels are associated with different features of the symbol (e.g.,dark bars and light spaces). In some areas, the signal may not bebimodal, such as for areas that do not include symbol features. In suchareas, the pixels may include just noise. Therefore, to avoid processinga very small contrast, the techniques can limit the contrastcomputations to avoid processing (e.g., amplifying) noise. For example,as discussed further in conjunctions with FIGS. 18-19, if noise isamplified it can appear in the reduced-bit images (e.g., and cannegatively affect image processing algorithms).

FIG. 15 shows a graph of an exemplary weighted histogram 1500 used toillustrate an intensity calculation, according to some embodiments. FIG.16 shows a graph of an exemplary histogram 1600 used to illustrate aweighted local histogram calculation, according to some embodiments.Both FIGS. 15-16 show the distribution of the gray levels of pixels inthe local neighborhood of a 12 bit HDR image. As discussed herein, thepixels in the local neighborhood will include data for different symbolfeatures (e.g., some bars and spaces) in the neighborhood. Depending onthe resolution, the pixels may also correspond to transition areas inthe symbol between symbol features (e.g., those in between as transitionfrom bar to a space). The peaks in the figures (e.g., peaks 1506 and1508 in FIG. 15, discussed further below) occur because some symbolfeatures are dark (e.g., the black bars) and light (e.g., the whitespaces).

Referring to FIG. 15, the weighted histogram 1500 is of the localneighborhood of an exemplary bimodal barcode region. The x axis 1502shows the pixel intensity values for a 12 bit HDR image, where theintensity of each pixel can range from 0 to 4095. The y axis 1504 showsthe weighted number of pixels with a particular intensity value. Thefirst peak 1506 of the weighted histogram occurs because of the darkfeatures (e.g., bars). The second peak 1508 of the weighted histogramoccurs because of the light features (e.g., spaces). The weightedhistogram 1500 shows the local contrast 1510 and the intensity 1512computed using a weighted standard deviation metric. The local contrast1510 is computed by calculating three standard deviations of the pixelvalues in the local region. The intensity 1512 is computed by taking theaverage of the pixel values in the local region. In this example, thepeak 1506 is approximately 615, the peak 1508 is approximately 2500, theintensity 1510 is approximately 1500, and the contrast is approximately2200.

Referring to FIG. 16, the weighted histogram 1600 is of the localneighborhood of an exemplary bimodal barcode region. The x axis 1602shows the pixel intensity values for a 12 bit HDR image, where theintensity of each pixel can range from 0 to 4095. The y axis 1604 showsthe weighted number of pixels with a particular intensity value. Thefirst peak 1606 of the weighted histogram occurs because of the darkfeatures (e.g., bars). The second peak 1608 of the weighted histogramoccurs because of the light features (e.g., spaces). The weightedhistogram 1500 shows the tails of the histogram, namely the bottom 20%of the histogram 1610 the top 20% of the histogram 1612. The average ofthe values in the bottom 20% 1610 is shown as 1618, and the average ofthe values in the top 20% 1612 is shown as 1620. The contrast 1616 ismeasured as the distance between the tail averages 1618 and 1620. Theintensity 1622 is the center of the contrast 1616.

While FIG. 15 includes the histogram 1500 to compare to the weightedstandard deviation calculation for the contrast 1512, both the intensity1510 (e.g., computed using a weighted average) and contrast 1512 can becomputed without calculating the actual histogram 1500 as discussedabove. In some embodiments, referring to FIG. 16, instead of determiningthe tails of the histogram as discussed above, the intensity andcontrast can be computed by building a weighted local histogram,locating the peaks 1606 and 1608 (e.g., based on the modes of the localhistogram), determining the trough 1614 between the peaks 1606 and 1608,and determining the distance 1616 between the peaks 1606 and 1608.

In some embodiments, one or more portions of the HDR image can beexcluded from processing. For example, the processing device can beconfigured to determine whether the local neighborhood has any featuresto amplify (or not). Such processing can effectively determine an imagemask, where the mask indicates portions of the image that are notprocessed (e.g., skipped) using the techniques described herein. In someembodiments, the processing device can perform a statistical test todetermine whether to process portions of the image (e.g., localneighborhoods of the image). For example, the processing device canmeasure the normality of the data in each neighborhood, such as bydetermining whether the data in the neighborhood meets a normaldistribution, as is known in the art. For example, the techniquesdescribed in Ghasemi and Zahediasl, “Normality Tests for StatisticalAnalysis: A guide for Non-Statisticians,” International Journal ofEndocrinology & Metabolism (20120 can be used, which is herebyincorporated by reference herein in their entirety.

FIG. 19 shows images to illustrate an example of excluding one or moreportions of the HDR image from processing, according to someembodiments. In particular, FIG. 19 shows a first tone-mapped image 1900with a 2D barcode 1902 and text 1904. The processing device did notexclude the areas surrounding the 2D barcode 1902 and text 1904, such asthe area 1906, from processing. As a result, the tone-mapped image 1900includes a black and white speckled pattern around the 2D barcode 1902and the text 1904. The second tone-mapped image 1950 also includes the2D barcode 1902 and the text 1904. The processing device excluded thesolid area 1952 around the 2D barcode 1902 and the text 1904 fromprocessing. As a result, the solid area 1952 reflects a mask of theareas of the original image that the processing device did not process.

Referring back to FIG. 14, at step 1410 the processing device applieslocal tone mapping based on the parameters determined in the previoussteps (e.g., the parameters determined in steps 1406 and 1408). Thelocal tone mapping compresses a high dynamic range (e.g., of theacquired HDR image) to a lower-dynamic range image, such as from a 12,14 or 16-bit HDR image to an 8-bit image. For example, the followingequation can be used to apply local tone mapping:

p8(i,j)=(y1+y2)/2+(y2−y1)×(p12(i,j)−intensity(i,j))/contrast(i,j))  (Equation2)

where:

p12(i,j) is the raw intensity value of the 12-bit image at location(i,j);

intensity(i,j) is a computed intensity of the pixel at location (i,j)based on the neighborhood of pixels around location (i,j);

contrast(i,j) is a computed contrast of the pixel at location (i,j)based on the neighborhood of pixels around location (i,j);

p8(i,j) is the corresponding pixel intensity value in the resulting8-bit image; and

(y1,y2) is the range of values in an 8-bit image over which the centerportion of the piecewise linear function stands, as discussed further inconjunction with FIG. 17.

As shown in Equation 2, the local tone mapping incorporates bothintensity(i,j) and contrast(i,j). The contrast(i,j) can reflect howspread the pixel values are in the neighborhood, as discussed herein.The contrast can be affected by, for example, the lighting during imagecapture, how the symbol was printed/etched, and/or the like. Thecontrast and intensity can vary over the image. For example, the imagemay include dark portions where the symbol has a certain contrast (e.g.,a high or low contrast), and the image may also include light/brightportions with a similar contrast as the dark region. As another example,in images the overall lighting might be high or low, while having asimilar contrast. By incorporating the contrast, the techniques can, forexample, maximize the contrast in regions of the image regardless of theimage intensity. The techniques can discount the average intensity basedon the middle gray level (e.g., where the values may range from 0-256,but the average intensity of the image can map to the middle of therange at 128).

At step 1412, the processing device can be configured to adjust thepixel intensity calculated in step 1410. For example, the processingdevice can be configured to stretch the p8(i,j) intensity values so thatthe values utilize the full 8-bit range. FIG. 17 is a graph 1700 showingan exemplary mapping of 12 bit values to 8 bit values, according to someembodiments. The x axis 1702 shows 12 bit values ranging from 0 to 2¹²(i.e., 4095), and the y axis 1704 shows 8 bit values ranging from 0 to2⁸ (i.e., 255). The curve 1706 shows a mapping of the 12 bit values onthe x axis 1702 to the 8 bit values on the y axis. The vertical line1708 shows intensity, and the arrow 1710 shows the contrast range, whichranges between 1712 and 1714 (e.g., a minimum and maximum from theintensity 1708). The section 1716 is below the contrast range 1710, andthe section 1718 is above the contrast range 1710.

Generally, if the symbol is bimodal, e.g., as shown in FIGS. 15-16, thehistogram will have two peaks, one for dark bars (the lower peak) andone for the light spaces (the higher peak). The system can be configuredto map values from the x-axis 1702 scale of 0-4095 to the y-axis 1704scale of 0-255 such that i) a space is mapped to a high value in the0-255 range, ii) a bar is mapped to a low value in the 0-255 range, iii)the values between a bar and space are stretched out as best as possible(e.g., since originally the image is an HDR image with more range thanthe resulting 8-bit (or lower bit) image), and/or the like. In order tostretch out the range of values between the bar and space, values thatare brighter than brightest space in the symbol or darker than darkestbar in the symbol are compressed above y2 and below y1, respectively.FIG. 17 graphically illustrates this mapping, where 1708 shows theaverage intensity and 1710 shows a measure of contrast between dark barsand light spaces (e.g., computed using a standard deviation as discussedherein). The y values along the y axis 1704 are mapped so that the yaxis is mapped between y1 and y2, where y1 is near the bottom of therange (e.g., 10, 5, etc.) and y2 is near the top of the range (e.g.,245, 250, etc.). By mapping between y1 and y2 in this manner, thetechniques can compress the parts of signal that are not relevant toanalyzing the symbol (e.g., decoding the symbol).

FIG. 18 shows exemplary images processed using various tone mappingtechniques, according to some embodiments. Images 1802 and 1804 areoriginal images, clipped to include the most significant bits. Images1812 and 1814 are images processed using intensity-based local tonemapping (e.g., discussed in conjunction with FIG. 3). Images 1822 and1824 are images processed using contrast-based local tone mapping (e.g.,discussed in conjunction with FIG. 14). As shown by images 1802 and1804, simply clipping the bit values results in the least benefit (e.g.,the 2D barcode in 1802 is not visible, and the linear barcode in 1804includes portions that are unlikely to be decoded during processing(e.g., portion 1806). As shown by images 1812 and 1814, theintensity-based local tone mapping techniques result in better images,including the 2D barcode and text being visible in image 1812, and animproved linear barcode, including portion 1816 in image 1814. In thisexample, the contrast-based local tone mapping achieved the bestreduced-bit images, such that the 2D barcode and text in image 1822 ismore visible than in image 1812, and the linear barcode in image 1824stands out from the background around the barcode.

Images 1822 and 1824 include amplified noise in certain areas, such asportions 1826 and 1828. As discussed above in conjunction with FIG. 19,portions of the HDR image can be eliminated from processing to avoidamplifying noise of areas that do not include the symbol of interest.Such processing is optional, since the noise amplification may notnegatively affect image processing algorithms (e.g., as shown in images1822 and 1824, the symbol information may be sufficiently processed toallow symbol decoding, inspection, etc.).

The tone mapping techniques discussed herein can be configured, asdescribed, such that the techniques do not require heavy computationresources, heavy memory access, and/or the like. Therefore, the tonemapping techniques can be designed for execution on various platformswith limited computation resources, such as FPGAs.

Techniques operating according to the principles described herein may beimplemented in any suitable manner. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. Such processorsmay be implemented as integrated circuits, with one or more processorsin an integrated circuit component, including commercially availableintegrated circuit components known in the art by names such as CPUchips, GPU chips, FPGA chips, microprocessor, microcontroller, orco-processor. Alternatively, a processor may be implemented in customcircuitry, such as an ASIC, or semicustom circuitry resulting fromconfiguring a programmable logic device. As yet a further alternative, aprocessor may be a portion of a larger circuit or semiconductor device,whether commercially available, semi-custom or custom. As a specificexample, some commercially available microprocessors have multiple coressuch that one or a subset of those cores may constitute a processor.Though, a processor may be implemented using circuitry in any suitableformat.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a PersonalDigital Assistant (PDA), a smart phone or any other suitable portable orfixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.In the embodiment illustrated, the input/output devices are illustratedas physically separate from the computing device. In some embodiments,however, the input and/or output devices may be physically integratedinto the same unit as the processor or other elements of the computingdevice. For example, a keyboard might be implemented as a soft keyboardon a touch screen. Alternatively, the input/output devices may beentirely disconnected from the computing device, and functionallyintegrated through a wireless connection.

Such computers may be interconnected by one or more networks in anysuitable form, including as a local area network or a wide area network,such as an enterprise network or the Internet. Such networks may bebased on any suitable technology and may operate according to anysuitable protocol and may include wireless networks, wired networks orfiber optic networks.

Also, the various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools, and also may becompiled as executable machine language code or intermediate code thatis executed on a framework or virtual machine.

In this respect, the invention may be embodied as a computer readablestorage medium (or multiple computer readable media) (e.g., a computermemory, one or more floppy discs, compact discs (CD), optical discs,digital video disks (DVD), magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe invention discussed above. As is apparent from the foregoingexamples, a computer readable storage medium may retain information fora sufficient time to provide computer-executable instructions in anon-transitory form. Such a computer readable storage medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present application as discussedabove. As used herein, the term “computer-readable storage medium”encompasses only a computer-readable medium that can be considered to bea manufacture (i.e., article of manufacture) or a machine. Alternativelyor additionally, the invention may be embodied as a computer readablemedium other than a computer-readable storage medium, such as apropagating signal.

The terms “code”, “program” or “software” are used herein in a genericsense to refer to any type of computer code or set ofcomputer-executable instructions that can be employed to program acomputer or other processor to implement various aspects of the presentapplication as discussed above. Additionally, it should be appreciatedthat according to one aspect of this embodiment, one or more computerprograms that when executed perform methods of the present applicationneed not reside on a single computer or processor, but may bedistributed in a modular fashion amongst a number of different computersor processors to implement various aspects of the present application.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconveys relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Various aspects of the present application may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Also, the invention may be embodied as a method, of which an example hasbeen provided. The acts performed as part of the method may be orderedin any suitable way. Accordingly, embodiments may be constructed inwhich acts are performed in an order different than illustrated, whichmay include performing some acts simultaneously, even though shown assequential acts in illustrative embodiments.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

What is claimed is:
 1. A method for reading a barcode symbol, the methodcomprising: acquiring a raw image of a barcode symbol, where each pixelof the raw image has a digital value having a raw bit depth; identifyinga region-of-interest of the raw image; determining a local pixelneighborhood metric for at least one raw pixel in the region-of-interestbased on an attribute of a symbology of the barcode in the raw image,wherein the local pixel neighborhood metric identifies on one or moreraw pixels near the at least one raw pixel; determining a local mappingfunction for the at least one raw pixel that maps the value of the rawpixel to a mapped pixel value with a mapped bit depth that is smallerthan the bit depth associated with the raw image, wherein the localmapping function is based on: a value of a set of raw pixels near the atleast one raw pixel within the local pixel neighborhood metric; and atleast one parameter determined based on the raw image; computing amapped image for the region-of-interest, comprising determining a pixelvalue for at least one mapped pixel value in the mapped image byapplying the local mapping function to the raw pixel value in the rawimage to generate the mapped pixel value; and decoding the barcodesymbol using the mapped image.
 2. The method of claim 1, wherein theintensity value is determined by applying a Gaussian kernel with akernel size determined based on the local pixel neighborhood metric, andcomputing the mapped image for the region of interest comprises applyingthe local mapping function, including the Gaussian kernel with thedetermined kernel size, to the raw pixel value in the raw image togenerate the mapped pixel value.
 3. The method of claim 1, whereinidentifying a region-of-interest comprises: identifying a region withinthe raw image containing the barcode symbol; identifying the entire rawimage; identifying a predetermined region in the raw image; or somecombination thereof.
 4. The method of claim 1, wherein the method isconfigured so that the method can be executed by an FPGA.
 5. The methodof claim 1, wherein the local mapping function depends on pixel valuesof all raw pixel values within the local pixel neighborhood metric. 6.The method of claim 5, where the local mapping function is a Reinhardoperator.
 7. The method of claim 1, wherein computing the mapped imagecomprises applying a different mapping function to each raw pixel in theregion-of-interest.
 8. The method of claim 1, wherein the attributecomprises at least a minimum size, a maximum size, or both.
 9. Themethod of claim 8, wherein the attribute comprises one or more of amodule size range, a feature size range, a symbol width range, or asymbol height range.
 10. The method of claim 1, wherein the attribute isdetermined based on a second image of a barcode symbol.
 11. The methodof claim 10, wherein the second image is a previous image acquired ofthe same barcode symbol, an image acquired of a different barcode symbolduring a tuning phase, or some combination thereof.
 12. The method ofclaim 1, wherein the at least one parameter is determined based on asecond image of a barcode symbol.
 13. The method of claim 12, whereinthe second image is a previous image acquired of the same barcodesymbol, an image acquired of a different barcode symbol, during a tuningphase, or some combination thereof.
 14. The method of claim 1, furthercomprising applying a global tone mapping technique to a pixel value forat least one mapped pixel value in the mapped image by applying a globalmapping function to the raw pixel value in the raw image to generate themapped pixel value.
 15. The method of claim 1, further comprising tuningthe global mapping function, the local mapping function, or both, forthe image.
 16. The method of claim 1, further comprising determining thevalue, comprising: determining an intensity of the set of raw pixels;and determining a contrast based on intensity values of each pixel inthe set of pixels.
 17. The method of claim 16, wherein determining theintensity of the set of raw pixels comprises determining a weightedaverage of the intensity values of each pixel in the set of pixels. 18.The method of claim 16, wherein determining the contrast comprisesdetermining a weighted standard deviation of the intensity values ofeach pixel in the set of pixels.
 19. An apparatus for reading a barcodesymbol, the apparatus comprising a processor in communication withmemory, the processor being configured to execute instructions stored inthe memory that cause the processor to: acquire a raw image of a barcodesymbol, where each pixel of the raw image has a digital value having araw bit depth; identify a region-of-interest of the raw image; determinea local pixel neighborhood metric for at least one raw pixel in theregion-of-interest based on an attribute of a symbology of the barcodein the raw image, wherein the local pixel neighborhood metric identifieson one or more raw pixels near the at least one raw pixel; determine alocal mapping function for the at least one raw pixel that maps thevalue of the raw pixel to a mapped pixel value with a mapped bit depththat is smaller than the bit depth associated with the raw image,wherein the local mapping function is based on: a value of a set of rawpixels near the at least one raw pixel within the local pixelneighborhood metric; and at least one parameter determined based on theraw image; compute a mapped image for the region-of-interest, comprisingdetermining a pixel value for at least one mapped pixel value in themapped image by applying the local mapping function to the raw pixelvalue in the raw image to generate the mapped pixel value; and decodethe barcode symbol using the mapped image.
 20. At least onenon-transitory computer-readable storage medium storingprocessor-executable instructions that, when executed by at least onecomputer hardware processor, cause the at least one computer hardwareprocessor to perform the acts of: acquiring a raw image of a barcodesymbol, where each pixel of the raw image has a digital value having araw bit depth; identifying a region-of-interest of the raw image;determining a local pixel neighborhood metric for at least one raw pixelin the region-of-interest based on an attribute of a symbology of thebarcode in the raw image, wherein the local pixel neighborhood metricidentifies on one or more raw pixels near the at least one raw pixel;determining a local mapping function for the at least one raw pixel thatmaps the value of the raw pixel to a mapped pixel value with a mappedbit depth that is smaller than the bit depth associated with the rawimage, wherein the local mapping function is based on: a value of a setof raw pixels near the at least one raw pixel within the local pixelneighborhood metric; and at least one parameter determined based on theraw image; computing a mapped image for the region-of-interest,comprising determining a pixel value for at least one mapped pixel valuein the mapped image by applying the local mapping function to the rawpixel value in the raw image to generate the mapped pixel value; anddecoding the barcode symbol using the mapped image.